Factors Influencing Designers' Adoption of AI-Generated Content Tools: Task Characteristics, Technology Characteristics

As a cutting-edge technology, AI-generated content (AIGC) has fundamentally altered the landscape of design by transforming its traditional creation methodologies. However, the underlying factors influencing designers’ adoption of such technologies remain largely unexplored. Previous research on the acceptance of AIGC tools primarily applied the conventional Technology Acceptance Model (TAM), disregarding the intelligent feature of artificial intelligence (AI) technologies. Given this research gap, this study introduces an integrated model to study the acceptance of AIGC tools by incorporating the Al device use acceptance model (AIDUA) and Task-technology fit model (TTF). Research hypotheses were proposed after an exhaustive analysis of the features of AIGC tools and designers’ needs. Subsequently, 412 responses were gathered from designers across diverse fields via an online questionnaire. Structural Equation Modelling (SEM) is employed to unveil the complexity within the process of adopting AIGC tools. The analysis is structured into three stages: primary appraisal, secondary appraisal, and the outcome stage. In the secondary appraisal, results show that task-technology fit, performance expectancy, and effort expectancy all positively influence emotions. In the last phase, the outcome stage, it is found that emotions positively influence users’ adoption intention and negatively influence the rejection intention. A notable discovery of this research is the substantial role of hedonic motivation in impacting performance expectancy and effort expectancy, which distinguishes itself from prior research results. These findings provide valuable insights for the design and development of AIGC tools, thereby boosting designers’ adoption of AIGC tools.